Statistical analysis of comparative data on composition and agronomic characteristics: new software tool and recurring issues identified

Transcription

1 Statistical analysis of comparative data on composition and agronomic characteristics: new software tool and recurring issues identified C. Paoletti*, J. Perry and H. Broll* * EFSA GMO Unit; EFSA GMO Panel EFSA info session on applications - GMO Parma, Italy 28 October 2014

2 GMO software tool scope of the project Development of a software for the implementation of the EFSA Guidance for risk assessment of food and feed from GM plants (EFSA, 2011) Tool for stakeholders to comply with EFSA GMO guidelines Harmonization of statistical analyses for GMO dossiers Harmonization of reporting outcome of the analyses

3 GMO software tool input data

4 GMO software tool access to the software

5 GMO software tool installation requirements

6 Main title Statistical evaluation of the field trials Contract with Hasselt University, Belgium Only field trial studies according to the latest EFSA Guidance, 2011 Dedicated time frame given for the evaluation (1 month) Two deliverables were requested from the contractor on each dossier: 1st deliverable: Evaluation report on the field trials highlighting issues for EFSA consideration (preparatory work) 2nd deliverable: Support the evaluation of the applicant s reply to EFSA questions

7 Main title Statistical evaluation of the field trials recurring issues Experimental design No information on randomization and blinding Requested in guidance but not always provided Exclusions Site exclusions: e.g. 10 included but only 8 analyzed: no proper explanation Sample exclusions: often not justified Can have implication on the minimum number of replicates

8 Main title Statistical evaluation of the field trials recurring issues Statistical analysis No statistical analysis plan (SAP) provided In line with good statistical practice Execution of R and SAS programs Re-analysis can not be carried out due to lack complete directory structure Values below Limit of Detection Substitution of ½ the detection limit Statistically, not the most appropriate way to handle non-detects, especially when frequency is medium high (e.g. around 30%)

9 Main title Statistical evaluation of the field trials recurring issues Statistical analysis Discrepancies found between the statistical outcome of the applicant and the re-analysis done by the Hasselt University Different outcome types identified No discussion of missing data mechanism (MCAR, MAR, MNAR) In line with good statistical practice When no equivalence limits can be established, often biological relevance is not discussed

10 Statistical evaluation of the field trials recurring issues Biological relevance example of an insufficient discussion All observed differences in this statistical analysis conducted under EFSA guidelines were characterized by a lack of compositional relevance from a food or feed perspective. This lack of relevance from a food or feed perspective is characterized by the small magnitudes of difference between the GM event (treated or not treated) and the control.

11 Main title Statistical evaluation of the field trials recurring issues Biological relevance expectations The applicant should report and discuss all significant differences observed between the GM plant, its comparator and, where applicable, any other test material, focusing on their biological relevance OECD listed endpoints are the basis. They serve as indicators for other parameters not measured but with safety relevance Consideration of the specific metabolic function/pathway

12 Main title Statistical evaluation of the field trials recurring issues Statistical analysis - Verification of assumptions Diagnostic Checks Assumption of normality of data and homogeneity of variance should always be checked Sometimes, only graphical can be subjective Some form of studentized/standardized residual used, but no proper justification is provided for the specific cut-off that is used When outliers found, analysis with and without outliers not always done Goodness-of-fit checks rarely done Linear mixed models have completely different theoretical implications on diagnostic checks than standard (fixed-effects) regression analysis. Conditional vs. marginal residuals Tests on normality, homogeneity of variance Box-Cox procedure

13 EFSA GMO Unit Thank you for your attention! 13